Genetic algorithms as function optimizers

We survey the use of genetic algorithms in function optimization. We find that there are two distinct camps. Holland's camp seems to be interested in artificial intelligence uses of genetic algorithms and seems to have made only a few attempts at function optimization. Rechenberg's camp has concentrated on the optimization of real-valued functions. The two camps use different methods and it seems difficult to compare their results. We include an annotated bibliography of some 70 items

Optimal economic operation of electric power systems using genetic based algorithms.

The thesis explores the potential of Genetic Algorithms (GAs) for optimising the operation of electric power systems. It discusses methods which have resulted in significant direct cost saving in operating an electric power system. In particular, the thesis demonstrates the simple search procedure and the powerful search ability of Gas in multi-modal, multi-objective problems, which are resisted by the most well known conventional techniques. Special emphasis has been given to the effectiveness of the enhanced genetic based algorithms and the importance of sophisticated problem structures. Finally, the feasibility and suitability of genetic based algorithms for power system optimisations are verified on a real power supply system. The basic requirement in operating a power system is to ensure that the whole system is run at the minimum possible cost, and the lowest possible pollution level, while reliability and security are maintained. These requirements have resulted in a wide range of power system optimisation problems. In this work, a selection of problems concerning operation economy, security and environmental impact have been dealt with by Genetic Algorithms. These problems are in order of increasing complexity as the project progresses: they range from static problems to dynamic problems, single objective to multi-objectives, softly constrained problems to harshly constrained problems, simple problem structure to more rigorous problem structure. Despite the diversity, GAs consistently produce solutions comparable to conventional techniques over the wide range of problem spectrum. It has been clearly demonstrated that a sophisticated problem structure can bring significant financial benefits in system operation, it has however added further complexity to the problem, where the best result may only be sought from the genetic based algorithms. The enhancements of Genetic Algorithms have been investigated with the aim of further improving the quality and speed of the solution. They have been enhanced in two levels: the first is to develop advanced genetic strategies, and this is subsequently refined by choosing optimal parameter values to further improve the strategies. The outcome of the study clearly indicates that genetic based algorithms are very attractive techniques for solving the ever more complicated optimisations of electric power systems. The basic requirement in operating a power system is to ensure that the whole system is run at the minimum possible cost, and the lowest possible pollution level, while reliability and security are maintained. These requirements have resulted in a wide range of power system optimisation problems. In this work, a selection of problems concerning operation economy, security and environmental impact have been dealt with by Genetic Algorithms. These problems are in order of increasing complexity as the project progresses: they range from static problems to dynamic problems, single objective to multi-objectives, softly constrained problems to harshly constrained problems, simple problem structure to more rigorous problem structure. Despite the diversity, GAs consistently produce solutions comparable to conventional techniques over the wide range of problem spectrum. It has been clearly demonstrated that a sophisticated problem structure can bring significant financial benefits in system operation, it has however added further complexity to the problem, where the best result may only be sought from the genetic based algorithms. The enhancements of Genetic Algorithms have been investigated with the aim of further improving the quality and speed of the solution. They have been enhanced in two levels: the first is to develop advanced genetic strategies, and this is subsequently refined by choosing optimal parameter values to further improve the strategies. The outcome of the study clearly indicates that genetic based algorithms are very attractive techniques for solving the ever more complicated optimisations of electric power systems

Investigations into distributed artificial intelligence techniques for design with applications to instruments

This Thesis is concerned with the application of Distributed Artificial Intelligence techniques for the design of instruments. In this thesis it is argued that, the early stages of the design process can be automated by the use of Distributed Artificial Intelligence systems that are contractual in their communication and control. A Distributed Problem Solver is proposed, and implemented, for the purpose of conceptual design of instruments. The system consists of a community of knowledge-based agents, with expertise on design of instrument sub-systems. The agents, use a task-sharing form of cooperation for dynamic problem decomposition and sub-problem distribution phases of the design problem solving. New design concepts are generated by suitable combination of partial solutions. To incorporate learning capabilities into our Distributed Problem Solver, we have proposed the use of Classifier System Modules as inductive knowledge-based agents. The application of Classifier Systems and Genetic Algorithms in the context of a number of concrete instrument design problems is investigated. A normalized formulation is applied to the multi-modal design optimization of a Linear Variable Differential Transformer. A number of important proposals for the application of classifier systems to the design automation of instruments are detailed. In particular, an implemented classifier system is used for the purpose of design heuristic extraction for corrugated diaphragms, using a set of dimensionless curves. In this application, the classifier system has produced a set of useful design heuristics by direct interactions with the specified mathematical model

A probabilistic cooperative-competitive hierarchical search model.

by Wong Yin Bun, Terence.Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.Includes bibliographical references (leaves 99-104).Abstract also in Chinese.List of Figures --- p.ixList of Tables --- p.xiChapter I --- Preliminary --- p.1Chapter 1 --- Introduction --- p.2Chapter 1.1 --- Thesis themes --- p.4Chapter 1.1.1 --- Dynamical view of landscape --- p.4Chapter 1.1.2 --- Bottom-up self-feedback algorithm with memory --- p.4Chapter 1.1.3 --- Cooperation and competition --- p.5Chapter 1.1.4 --- Contributions to genetic algorithms --- p.5Chapter 1.2 --- Thesis outline --- p.5Chapter 1.3 --- Contribution at a glance --- p.6Chapter 1.3.1 --- Problem --- p.6Chapter 1.3.2 --- Approach --- p.7Chapter 1.3.3 --- Contributions --- p.7Chapter 2 --- Background --- p.8Chapter 2.1 --- Iterative stochastic searching algorithms --- p.8Chapter 2.1.1 --- The algorithm --- p.8Chapter 2.1.2 --- Stochasticity --- p.10Chapter 2.2 --- Fitness landscapes and its relation to neighborhood --- p.12Chapter 2.2.1 --- Direct searching --- p.12Chapter 2.2.2 --- Exploration and exploitation --- p.12Chapter 2.2.3 --- Fitness landscapes --- p.13Chapter 2.2.4 --- Neighborhood --- p.16Chapter 2.3 --- Species formation methods --- p.17Chapter 2.3.1 --- Crowding methods --- p.17Chapter 2.3.2 --- Deterministic crowding --- p.18Chapter 2.3.3 --- Sharing method --- p.18Chapter 2.3.4 --- Dynamic niching --- p.19Chapter 2.4 --- Summary --- p.21Chapter II --- Probabilistic Binary Hierarchical Search --- p.22Chapter 3 --- The basic algorithm --- p.23Chapter 3.1 --- Introduction --- p.23Chapter 3.2 --- Search space reduction with binary hierarchy --- p.25Chapter 3.3 --- Search space modeling --- p.26Chapter 3.4 --- The information processing cycle --- p.29Chapter 3.4.1 --- Local searching agents --- p.29Chapter 3.4.2 --- Global environment --- p.30Chapter 3.4.3 --- Cooperative refinement and feedback --- p.33Chapter 3.5 --- Enhancement features --- p.34Chapter 3.5.1 --- Fitness scaling --- p.34Chapter 3.5.2 --- Elitism --- p.35Chapter 3.6 --- Illustration of the algorithm behavior --- p.36Chapter 3.6.1 --- Test problem --- p.36Chapter 3.6.2 --- Performance study --- p.38Chapter 3.6.3 --- Benchmark tests --- p.45Chapter 3.7 --- Discussion and analysis --- p.45Chapter 3.7.1 --- Hierarchy of partitions --- p.45Chapter 3.7.2 --- Availability of global information --- p.47Chapter 3.7.3 --- Adaptation --- p.47Chapter 3.8 --- Summary --- p.48Chapter III --- Cooperation and Competition --- p.50Chapter 4 --- High-dimensionality --- p.51Chapter 4.1 --- Introduction --- p.51Chapter 4.1.1 --- The challenge of high-dimensionality --- p.51Chapter 4.1.2 --- Cooperation - A solution to high-dimensionality --- p.52Chapter 4.2 --- Probabilistic Cooperative Binary Hierarchical Search --- p.52Chapter 4.2.1 --- Decoupling --- p.52Chapter 4.2.2 --- Cooperative fitness --- p.53Chapter 4.2.3 --- The cooperative model --- p.54Chapter 4.3 --- Empirical performance study --- p.56Chapter 4.3.1 --- pBHS versus pcBHS --- p.56Chapter 4.3.2 --- Scaling behavior of pcBHS --- p.60Chapter 4.3.3 --- Benchmark test --- p.62Chapter 4.4 --- Summary --- p.63Chapter 5 --- Deception --- p.65Chapter 5.1 --- Introduction --- p.65Chapter 5.1.1 --- The challenge of deceptiveness --- p.65Chapter 5.1.2 --- Competition: A solution to deception --- p.67Chapter 5.2 --- Probabilistic cooperative-competitive binary hierarchical search --- p.67Chapter 5.2.1 --- Overview --- p.68Chapter 5.2.2 --- The cooperative-competitive model --- p.68Chapter 5.3 --- Empirical performance study --- p.70Chapter 5.3.1 --- Goldberg's deceptive function --- p.70Chapter 5.3.2 --- "Shekel family - S5, S7, and S10" --- p.73Chapter 5.4 --- Summary --- p.74Chapter IV --- Finale --- p.78Chapter 6 --- A new genetic operator --- p.79Chapter 6.1 --- Introduction --- p.79Chapter 6.2 --- Variants of the integration --- p.80Chapter 6.2.1 --- Fixed-fraction-of-all --- p.83Chapter 6.2.2 --- Fixed-fraction-of-best --- p.83Chapter 6.2.3 --- Best-from-both --- p.84Chapter 6.3 --- Empricial performance study --- p.84Chapter 6.4 --- Summary --- p.88Chapter 7 --- Conclusion and Future work --- p.89Chapter A --- The pBHS Algorithm --- p.91Chapter A.1 --- Overview --- p.91Chapter A.2 --- Details --- p.91Chapter B --- Test problems --- p.96Bibliography --- p.9

An application of genetic algorithms to chemotherapy treatment.

The present work investigates methods for optimising cancer chemotherapy within the bounds of clinical acceptability and making this optimisation easily accessible to oncologists. Clinical oncologists wish to be able to improve existing treatment regimens in a systematic, effective and reliable way. In order to satisfy these requirements a novel approach to chemotherapy optimisation has been developed, which utilises Genetic Algorithms in an intelligent search process for good chemotherapy treatments. The following chapters consequently address various issues related to this approach. Chapter 1 gives some biomedical background to the problem of cancer and its treatment. The complexity of the cancer phenomenon, as well as the multi-variable and multi-constrained nature of chemotherapy treatment, strongly support the use of mathematical modelling for predicting and controlling the development of cancer. Some existing mathematical models, which describe the proliferation process of cancerous cells and the effect of anti-cancer drugs on this process, are presented in Chapter 2. Having mentioned the control of cancer development, the relevance of optimisation and optimal control theory becomes evident for achieving the optimal treatment outcome subject to the constraints of cancer chemotherapy. A survey of traditional optimisation methods applicable to the problem under investigation is given in Chapter 3 with the conclusion that the constraints imposed on cancer chemotherapy and general non-linearity of the optimisation functionals associated with the objectives of cancer treatment often make these methods of optimisation ineffective. Contrariwise, Genetic Algorithms (GAs), featuring the methods of evolutionary search and optimisation, have recently demonstrated in many practical situations an ability to quickly discover useful solutions to highly-constrained, irregular and discontinuous problems that have been difficult to solve by traditional optimisation methods. Chapter 4 presents the essence of Genetic Algorithms, as well as their salient features and properties, and prepares the ground for the utilisation of Genetic Algorithms for optimising cancer chemotherapy treatment. The particulars of chemotherapy optimisation using Genetic Algorithms are given in Chapter 5 and Chapter 6, which present the original work of this thesis. In Chapter 5 the optimisation problem of single-drug chemotherapy is formulated as a search task and solved by several numerical methods. The results obtained from different optimisation methods are used to assess the quality of the GA solution and the effectiveness of Genetic Algorithms as a whole. Also, in Chapter 5 a new approach to tuning GA factors is developed, whereby the optimisation performance of Genetic Algorithms can be significantly improved. This approach is based on statistical inference about the significance of GA factors and on regression analysis of the GA performance. Being less computationally intensive compared to the existing methods of GA factor adjusting, the newly developed approach often gives better tuning results. Chapter 6 deals with the optimisation of multi-drug chemotherapy, which is a more practical and challenging problem. Its practicality can be explained by oncologists' preferences to administer anti-cancer drugs in various combinations in order to better cope with the occurrence of drug resistant cells. However, the imposition of strict toxicity constraints on combining various anticancer drugs together, makes the optimisation problem of multi-drug chemotherapy very difficult to solve, especially when complex treatment objectives are considered. Nevertheless, the experimental results of Chapter 6 demonstrate that this problem is tractable to Genetic Algorithms, which are capable of finding good chemotherapeutic regimens in different treatment situations. On the basis of these results a decision has been made to encapsulate Genetic Algorithms into an independent optimisation module and to embed this module into a more general and user-oriented environment - the Oncology Workbench. The particulars of this encapsulation and embedding are also given in Chapter 6. Finally, Chapter 7 concludes the present work by summarising the contributions made to the knowledge of the subject treated and by outlining the directions for further investigations. The main contributions are: (1) a novel application of the Genetic Algorithm technique in the field of cancer chemotherapy optimisation, (2) the development of a statistical method for tuning the values of GA factors, and (3) the development of a robust and versatile optimisation utility for a clinically usable decision support system. The latter contribution of this thesis creates an opportunity to widen the application domain of Genetic Algorithms within the field of drug treatments and to allow more clinicians to benefit from utilising the GA optimisation